Introduction to GPU Architecture and Programming Training by Tonex
The Introduction to GPU Architecture and Programming Training by Tonex offers a comprehensive foundation for understanding modern GPU design, memory organization, and the essentials of general-purpose GPU (GPGPU) computing. This course highlights how GPU-centric thinking differs fundamentally from CPU paradigms, empowering engineers to optimize applications for high performance. Participants will explore key programming models like CUDA and OpenCL and learn how workload characteristics influence performance outcomes. Importantly, the training addresses the rising role of GPUs in cybersecurity, where massive parallelism accelerates encryption, threat analysis, and AI-based defenses, helping security professionals stay ahead of evolving cyber threats.
Audience:
- Software Developers
- System Architects
- Embedded Systems Engineers
- Data Scientists
- Cybersecurity Professionals
- IT Professionals and Analysts
Learning Objectives:
- Differentiate between GPU and CPU architectures
- Understand GPU memory models and their usage
- Grasp fundamentals of CUDA and OpenCL programming
- Identify workloads suitable for GPU acceleration
- Optimize computation and memory access for GPUs
- Recognize the impact of GPUs on cybersecurity strategies
Course Modules:
Module 1: Understanding GPU and CPU
- Key differences: GPU vs CPU
- Parallelism at scale
- Role of GPUs in modern computing
- Cybersecurity relevance of GPU acceleration
- CPU bottlenecks vs GPU advantages
- Emerging trends in processing architectures
Module 2: Inside Streaming Multiprocessors
- SMs structure and roles
- Thread execution and warps
- Instruction scheduling in SMs
- Load balancing challenges
- SMs and parallel computation efficiency
- Case studies: SM designs in leading GPUs
Module 3: GPU Memory Models Overview
- Global memory access and usage
- Shared memory optimization
- Local memory for individual threads
- Constant memory for read-only access
- Memory bandwidth and latency issues
- Best practices for memory hierarchy utilization
Module 4: Basics of CUDA Programming
- CUDA programming model overview
- Kernels and thread hierarchy
- Launching and managing threads
- Synchronization techniques
- Memory handling in CUDA
- Introduction to performance tuning
Module 5: Introduction to OpenCL Programming
- OpenCL programming concepts
- Platform and device management
- Memory object creation and handling
- Kernel development and execution
- Host-device communication in OpenCL
- Comparing OpenCL and CUDA use cases
Module 6: Workload Suitability for GPUs
- Identifying parallelizable workloads
- Compute-bound vs memory-bound tasks
- Factors influencing GPU performance
- Real-world examples across industries
- Impact of GPU acceleration on cybersecurity
- Choosing the right tasks for GPGPU computing
Gain an edge in high-performance computing and cybersecurity innovation with the Introduction to GPU Architecture and Programming Training by Tonex. Enroll now to build foundational skills that will empower you to design faster, smarter, and more secure applications in today’s GPU-driven world!